1. Field of the Invention
The present invention relates to managing job submissions in a compute environment such as a cluster and more specifically to intelligent data just in time data pre-staging to optimize the use of diverse compute resources.
2. Introduction
The present invention relates to a system and method of allocation resources in the context of a grid or cluster of computers. Grid computing may be defined as coordinated resource sharing and problem solving in dynamic, multi-institutional collaborations. Many computing projects require much more computational power and resources than a single computer may provide. Networked computers with peripheral resources such as printers, scanners, I/O devices, storage disks, scientific devices and instruments, etc. may need to be coordinated and utilized to complete a task.
Grid/cluster resource management generally describes the process of identifying requirements, matching resources to applications, allocating those resources, and scheduling and monitoring grid resources over time in order to run grid applications as efficiently as possible. Each project will utilize a different set of resources and thus is typically unique. In addition to the challenge of allocating resources for a particular job, grid administrators also have difficulty obtaining a clear understanding of the resources available, the current status of the grid and available resources, and real-time competing needs of various users. One aspect of this process is the ability to reserve resources for a job. A cluster manager will seek to reserve a set of resources to enable the cluster to process a job at a promised quality of service.
General background information on clusters and grids may be found in several publications. See, e.g., Grid Resource Management State of the Art and Future Trends, Jarek Nabrzyski, Jennifer M. Schopf, and Jan Weglarz, Kluwer Academic Publishers, 2004; and Beowulf Cluster Computing with Linux, edited by William Gropp, Ewing Lusk, and Thomas Sterling, Massachusetts Institute of Technology, 2003.
It is generally understood herein that the terms grid and cluster are interchangeable in that there is no specific definition of either. In general, a grid will comprise a plurality of clusters as will be shown in FIG. 1. Several general challenges exist when attempting to maximize resources in a grid. First, there are typically multiple layers of grid and cluster schedulers. A grid 100 generally comprises a group of clusters or a group of networked computers. The definition of a grid is very flexible and may mean a number of different configurations of computers. The introduction here is meant to be general given the variety of configurations that are possible. A grid scheduler 102 communicates with a plurality of cluster schedulers 104A, 104B and 104C. Each of these cluster schedulers communicates with a respective resource manager 106A, 106B or 106C. Each resource manager communicates with a respective series of compute resources shown as nodes 108A, 108B, 108C in cluster 110, nodes 108D, 108E, 108F in cluster 112 and nodes 108G, 108H, 108I in cluster 114.
Local schedulers (which may refer to either the cluster schedulers 104 or the resource managers 106) are closer to the specific resources 108 and may not allow grid schedulers 102 direct access to the resources. Examples of compute resources include data storage devices such as hard drives and computer processors. The grid level scheduler 102 typically does not own or control the actual resources. Therefore, jobs are submitted from the high level grid-scheduler 102 to a local set of resources with no more permissions that then user would have. This reduces efficiencies and can render the reservation process more difficult.
The heterogeneous nature of the shared resources also causes a reduction in efficiency. Without dedicated access to a resource, the grid level scheduler 102 is challenged with the high degree of variance and unpredictability in the capacity of the resources available for use. Most resources are shared among users and projects and each project varies from the other. The performance goals for projects differ. Grid resources are used to improve performance of an application but the resource owners and users have different performance goals: from optimizing the performance for a single application to getting the best system throughput or minimizing response time. Local policies may also play a role in performance.
An administrator can partition a cluster and identify a set of resources to be dedicated to a particular purpose and another set of resources can be dedicated to another purpose. In this regard, the resources are reserved in advance to process the job. To illustrate, an example is provided. Assume that the weather bureau needs to do a compute intensive hurricane analysis. They will desire to gather a large amount of stored data from disk and then process that data and store the resulting computed data. A scheduler, to manage the cluster resources for this job, will schedule the disks to retrieve the data, network routers with an appropriate bandwidth to transmit the data, computer processors to then process the data, and then network routers and data disks to transmit and store the computed data. The availability of the disks for these retrieval and storage aspects of the job may not overlap specifically in time with the time for the availability of the computer processing or transmission resources.
To manage the jobs submissions, a cluster scheduler will employ reservations to insure that jobs will have the resources necessary for processing. FIG. 1B illustrates a cluster/node diagram for a cluster 124 with nodes 120. Time is along the X axis. Node 1 has a reservation on it and an access control list (ACL) 122 which is static. The ACL 122 is based on the credential available to the requestor or person submitting the job. In other words, the user, group, the account, the class or quality of service the requestor has and/or is asking for. The job either will get onto the ACL 122 based on the criteria or it won't. That determination is made at the time the job is submitted for entry on the ACL 122.
The approach described above for reserving and processing jobs utilizing the various cluster resources has drawbacks in efficiency. The retrieved data from the disk storage resource may not coincide with the computer processing resources. In other words, the data may be retrieved from disk but the computer processors may not be ready to process the data given the other jobs submissions that are operating within their reservations on the cluster resources. To improve the management of cluster resources, what is needed in the art is an improved method for managing the consumption of diverse resources within a compute environment such as a cluster or grid.